AI SIEM: How SIEM with AI/ML is Revolutionizing the SOC

AI SIEM: How SIEM with AI/ML is Revolutionizing the SOC

What Is AI-Based SIEM? 

Traditional SIEM systems have long been the cornerstone of cybersecurity efforts, helping to consolidate, correlate, and analyze security data from various sources. However, with the growing sophistication of cyber threats and the sheer volume of security data, traditional SIEM systems have struggled to keep up. AI-based SIEM is an advanced form of security information and event management (SIEM) that uses the capabilities of artificial intelligence (AI) and machine learning (ML) to solve many of the challenges of the past. 

AI-based SIEM is a technology that not only automates the complex processes of data aggregation and normalization but also enables proactive threat detection and response through machine learning and predictive analytics. By learning from past security data and patterns, AI SIEM can predict and detect potential threats before they happen. Moreover, it can automate the incident response process, thereby minimizing the impact of security breaches.

In essence, AI SIEM provides an intelligent, automated, and proactive approach to threat detection and response.

Components of AI-Driven SIEM 

Data Aggregation, Normalization, and Enrichment

In the context of cybersecurity, data aggregation refers to the process of collecting security data from various sources, including network devices, servers, databases, applications, and more. This data can include logs, event data, threat intelligence, and other types of security-related information.

Normalization, on the other hand, is about converting this raw security data into a consistent, standardized format. This process is critical for ensuring that the AI SIEM system can accurately analyze and correlate the data, regardless of its source.

What makes AI SIEM stand out, however, is its ability to automate these processes. By leveraging AI and machine learning, AI SIEM can sort through data faster and intelligently aggregate and normalize security data, thereby significantly reducing the time and effort required for these tasks.

Data enrichment is the process of improving the accuracy and reliability of the data a SIEM collects. AI-powered SIEMs enhance data with additional information like threat intelligence to add context and improve the quality of the data which can then be used for better decision making. 

Learn more: read our detailed guide to SIEM threat intelligence 

Machine Learning and Pattern Recognition

Machine learning and pattern recognition enable SIEM to learn from past security data and patterns, enabling it to detect anomalies and potential threats that traditional SIEM systems that relied solely on signatures and criticalities from the log sources themselves might miss. 

For instance, AI SIEM can use machine learning algorithms to analyze historical security data, identify patterns and trends, and create a baseline of ‘normal’ behavior. It can then continuously monitor current security data against this baseline, enabling it to detect any deviations or anomalies that could indicate a potential threat.

Moreover, through pattern recognition, AI SIEM can identify correlation within logs associated with known threats or attack vectors. This capability allows SIEMs to detect and alert on potential threats in near real-time, thereby significantly reducing the time to detection and response.

Automated Incident Response

In the event of a detected threat or security breach, quick and effective response is critical to minimizing the impact. AI-based SIEM employs options for automation to streamline and accelerate the incident response process. It can automatically trigger alerts, implement predefined response actions, or even orchestrate complex response workflows. 

AI-based SIEM can provide security teams with detailed, actionable insights into the threat, helping them make informed decisions and take effective action.

Predictive Analytics

By analyzing historical security data and patterns, AI-based SIEM can predict potential future threats and vulnerabilities. This capability enables organizations to proactively secure their systems and data, rather than simply reacting to threats as they occur.

How AI and ML in SIEM are Revolutionizing Security Operations Centers

AI and machine learning (ML) are crucial components in enhancing SIEM capabilities. These technologies enable SIEM to proactively detect threats, respond efficiently, reduce false positives, and provide better insights into an organization’s security posture.

Enhanced Threat Detection

AI can analyze vast amounts of data in real-time to identify potential threats. It goes beyond the capabilities of traditional SIEM solutions by detecting even the most subtle anomalies that might indicate a security breach.

Moreover, AI can learn from past incidents, making it smarter and more accurate in detecting threats. This continuous learning and adaptation make AI-based SIEM systems robust and resilient against evolving cyber threats.

Improved Efficiency of Incident Response

In a traditional SIEM system, detecting a threat is just the beginning of the process. Security experts need to analyze it, decide on the appropriate response, and then execute that response. This process can be time-consuming, especially when dealing with numerous threats simultaneously.

With AI integrated into SIEM, the system can automate much of this process. AI can analyze a threat, decide the best response based on past data, and even execute that response. This automation significantly reduces the time taken to respond to a threat, potentially preventing it from causing significant damage.

Reduced False Positives

In a conventional SIEM system, false positives (legitimate events detected as suspicious security events) are a significant problem as they can distract security teams from real threats. As an example, if a firewall reports every attack made against it, whether or not that attack was appropriate to the technology employed or successful, the team still has to look at and evaluate each attack.

However, with the help of AI, the system can learn to differentiate between normal behavior, actual threats, and significant events vs a system performing as expected (i.e., blocking the noisy traffic). This capability results in fewer false positives, allowing security teams to focus on genuine threats.

Improved Insight into Security Posture

Lastly, AI provides improved insight into an organization’s security posture. AI-based SIEM uses advanced analytics to provide a more in-depth and accurate understanding of vulnerabilities and potential threats. It can also provide actionable recommendations to improve security.

Algorithms and Techniques AI-Based SIEM Uses to Detect Threats 

Deep Learning Algorithms

Deep Learning is a subset of machine learning that uses artificial neural networks to mimic the human brain’s decision-making process. In the context of AI SIEM, deep learning algorithms can analyze vast amounts of data and identify complex patterns that might signal a security threat.

These algorithms can process unstructured data such as documents, binary files, and images, making it possible to analyze a wide range of data sources for potential threats. They can also identify subtle patterns and correlations that might be missed by traditional rule-based systems, making them an invaluable tool for threat detection.

Natural Language Processing

Natural Language Processing (NLP) involves the use of computational techniques to understand and interpret human language. In the context of cybersecurity, NLP can be used to analyze text-based data such as system logs, network traffic, and user communications for potential threats. NLP exploration is already underway for many DLP vendors, helping them more accurately scan for data and word clusters associated with IP, employee harm, and other key indicators.

For example, NLP can analyze system logs to understand the normal functioning of a system and identify any deviations that might indicate a security threat. Similarly, it can analyze network traffic to detect suspicious activities such as data exfiltration or unauthorized access. NLP can also analyze user communications to detect potential insider threats or social engineering attacks.

User and Entity Behavior Analytics

User and Entity Behavior Analytics (UEBA) involves the use of ML algorithms to understand the normal behavior of users and entities (such as devices from servers and laptops, applications, and networks) and detect any deviations that might indicate a threat.

For example, UEBA can identify if a user is accessing sensitive data at unusual times or from unusual locations, which might indicate a potential security breach. Similarly, it can detect if a device is communicating with a suspicious IP address, indicating a potential malware infection. By understanding the normal behavior of users and entities, UEBA can detect subtle anomalies that might be missed by traditional rule- or signature-based systems.

Predictive Analytics

Predictive analytics involves using historical data to predict future events or trends. AI-based SIEM may use machine learning algorithms to analyze historical data, identify patterns, and predict potential threats.

Predictive analytics is especially useful in identifying potential threats before they occur. This enables organizations to take proactive measures to prevent security incidents. Moreover, predictive analytics can also help in prioritizing threats, enabling organizations to focus their resources on the most critical threats.

Exabeam Fusion: The Leading AI-Powered SIEM

Exabeam offers an AI-powered experience across the entire TDIR workflow. A combination of more than 1,800 pattern-matching rules and ML-based behavior models automatically detect potential security threats such as credential-based attacks, insider threats, and ransomware activity by identifying high risk user and entity activity. The industry-leading user and entity behavior analytics (UEBA) baselines normal activity for all users and entities, presenting all notable events chronologically.

Smart Timelines highlight the risk associated with each event, saving an analyst from writing hundreds of queries. Machine learning automates the alert triage workflow, adding UEBA context to dynamically identify, prioritize, and escalate alerts requiring the most attention.

The Exabeam platform can orchestrate and automate repeated workflows to over 100 third-party products with actions and operations, from semi- to fully automated activity. And Exabeam Outcomes Navigator maps the sources of the feeds that come into Exabeam products against the most common security use cases and suggests ways to improve coverage. 

See Exabeam in action: Learn more about Exabeam SIEM